Marco Anisetti

3.6k total citations · 1 hit paper
118 papers, 2.4k citations indexed

About

Marco Anisetti is a scholar working on Computer Vision and Pattern Recognition, Information Systems and Artificial Intelligence. According to data from OpenAlex, Marco Anisetti has authored 118 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Computer Vision and Pattern Recognition, 36 papers in Information Systems and 34 papers in Artificial Intelligence. Recurrent topics in Marco Anisetti's work include Cloud Data Security Solutions (16 papers), Access Control and Trust (14 papers) and Image and Signal Denoising Methods (13 papers). Marco Anisetti is often cited by papers focused on Cloud Data Security Solutions (16 papers), Access Control and Trust (14 papers) and Image and Signal Denoising Methods (13 papers). Marco Anisetti collaborates with scholars based in Italy, South Korea and United Arab Emirates. Marco Anisetti's co-authors include Ernesto Damiani, Paolo Ceravolo, Borko Furht, Gwanggil Jeon, Claudio A. Ardagna, Valerio Bellandi, Jechang Jeong, Awais Ahmad, Jiaji Wu and Marco Cremonini and has published in prestigious journals such as IEEE Transactions on Image Processing, IEEE Access and Sensors.

In The Last Decade

Marco Anisetti

109 papers receiving 2.3k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Augmented reality technologies, systems and applications

2010 950 citations Marco Anisetti, Ernesto Damiani et al. profile →

Peers

Marco Anisetti

CVPR

HI

IS

CNC

AI

Fabrizio Lamberti Italy

Timothy K. Shih Taiwan

Paolo Ceravolo Italy

Jukka Riekki Finland

Brian Lee Ireland

Wen-Huang Cheng Taiwan

Peter Corcoran Ireland

M. Anwar Hossain Saudi Arabia

Brian Meyers United States

Woontack Woo South Korea

Fabrizio Lamberti Italy

Marco Anisetti

968 ×0.8

CVPR

657 ×1.3

HI

733 ×1.6

IS

318 ×0.8

CNC

442 ×1.2

AI

Citations per year, relative to Marco Anisetti Marco Anisetti (= 1×) peers Fabrizio Lamberti

Countries citing papers authored by Marco Anisetti

Since Specialization

Citations

This map shows the geographic impact of Marco Anisetti's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Marco Anisetti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marco Anisetti more than expected).

Fields of papers citing papers by Marco Anisetti

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marco Anisetti. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Marco Anisetti. The network helps show where Marco Anisetti may publish in the future.

Co-authorship network of co-authors of Marco Anisetti

This figure shows the co-authorship network connecting the top 25 collaborators of Marco Anisetti. A scholar is included among the top collaborators of Marco Anisetti based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Marco Anisetti. Marco Anisetti is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Braghin, Chiara, et al.. (2025). Maximizing data quality while ensuring data protection in service-based data pipelines. Journal Of Big Data. 12(1).

2.

Anisetti, Marco, et al.. (2025). Protecting machine learning from poisoning attacks: A risk-based approach. Computers & Security. 155. 104468–104468. 2 indexed citations

3.

Berto, F., et al.. (2024). A Methodology for Web Cache Deception Vulnerability Discovery. Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano). 231–238. 1 indexed citations

4.

Ardagna, Claudio A., et al.. (2024). Assurance in Advanced 5G Edge Continuum. IEEE Access. 12. 178659–178671.

5.

Anisetti, Marco, Claudio A. Ardagna, Ernesto Damiani, & Nabil El Ioini. (2024). A Journey into Security Certification.

6.

Anisetti, Marco, et al.. (2024). Certifying Accuracy, Privacy, and Robustness of ML-Based Malware Detection. SN Computer Science. 5(6). 2 indexed citations

7.

Anisetti, Marco, et al.. (2024). A Transparent Certification Scheme Based on Blockchain for Service-Based Systems. Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano). 19. 501–511.

8.

Anisetti, Marco, et al.. (2023). An assurance process for Big Data trustworthiness. Future Generation Computer Systems. 146. 34–46. 6 indexed citations

9.

Anisetti, Marco, et al.. (2023). On the Robustness of Random Forest Against Untargeted Data Poisoning: An Ensemble-Based Approach. IEEE Transactions on Sustainable Computing. 8(4). 540–554. 13 indexed citations

10.

Anisetti, Marco, et al.. (2023). Advanced IoT Edge Architecture for Smart City. 46–53. 1 indexed citations

11.

Anisetti, Marco, et al.. (2022). A Security Certification Scheme for Information-Centric Networks. IEEE Transactions on Network and Service Management. 19(3). 2397–2408. 8 indexed citations

12.

Ahmed, Imran, Marco Anisetti, Awais Ahmad, & Gwanggil Jeon. (2022). A Multilayer Deep Learning Approach for Malware Classification in 5G-Enabled IIoT. IEEE Transactions on Industrial Informatics. 19(2). 1495–1503. 40 indexed citations

13.

Anisetti, Marco, et al.. (2022). An Artificial Intelligence Outlook for Colorectal Cancer Screening. Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano). 66–72. 2 indexed citations

14.

Taniguchi, Yoshio, et al.. (2022). Counseling (ro)bot as a use case for 5G/6G. Complex & Intelligent Systems. 8(5). 3899–3917. 4 indexed citations

15.

Ahmed, Imran, Marco Anisetti, & Gwanggil Jeon. (2021). An IoT‐based human detection system for complex industrial environment with deep learning architectures and transfer learning. International Journal of Intelligent Systems. 37(12). 10249–10267. 16 indexed citations

16.

Zhou, Yang, et al.. (2021). Gradient-based multi-focus image fusion method using convolution neural network. Computers & Electrical Engineering. 92. 107174–107174. 8 indexed citations

17.

Chen, Lihui, Xiaomin Yang, Gwanggil Jeon, Marco Anisetti, & Kai Liu. (2020). A trusted medical image super-resolution method based on feedback adaptive weighted dense network. Artificial Intelligence in Medicine. 106. 101857–101857. 43 indexed citations

18.

Anisetti, Marco, et al.. (2015). Toward Security and Performance Certification of Open Stack. 564–571. 21 indexed citations

19.

Anisetti, Marco, et al.. (2005). Localization and tracking of mobile antenna in urban environment. 4 indexed citations

20.

Anisetti, Marco, Valerio Bellandi, Ernesto Damiani, & Fabrizio Beverina. (2005). Facial identification problem: A tracking based approach. Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano). 28–35. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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